Host pathogen protein interactions predicted by comparative modeling

Pathogens have evolved numerous strategies to infect their hosts, while hosts have evolved immune responses and other defenses to these foreign challenges. The vast majority of host-pathogen interactions involve protein-protein recognition, yet our current understanding of these interactions is limited. Here, we present and apply a computational whole-genome protocol that generates testable predictions of host-pathogen protein interactions. The protocol first scans the host and pathogen genomes for proteins with similarity to known protein complexes, then assesses these putative interactions, using structure if available, and, finally, filters the remaining interactions using biological context, such as the stage-specific expression of pathogen proteins and tissue expression of host proteins. The technique was applied to 10 pathogens, including species of Mycobacterium, apicomplexa, and kinetoplastida, responsible for "neglected" human diseases. The method was assessed by (1) comparison to a set of known host-pathogen interactions, (2) comparison to gene expression and essentiality data describing host and pathogen genes involved in infection, and (3) analysis of the functional properties of the human proteins predicted to interact with pathogen proteins, demonstrating an enrichment for functionally relevant host-pathogen interactions. We present several specific predictions that warrant experimental follow-up, including interactions from previously characterized mechanisms, such as cytoadhesion and protease inhibition, as well as suspected interactions in hypothesized networks, such as apoptotic pathways. Our computational method provides a means to mine whole-genome data and is complementary to experimental efforts in elucidating networks of host-pathogen protein interactions.     

Computational prediction of host-pathogen protein-protein interactions

MOTIVATION: Infectious diseases such as malaria result in millions of deaths each year. An important aspect of any host-pathogen system is the mechanism by which a pathogen can infect its host. One method of infection is via protein-protein interactions (PPIs) where pathogen proteins target host proteins. Developing computational methods that identify which PPIs enable a pathogen to infect a host has great implications in identifying potential targets for therapeutics. RESULTS: We present a method that integrates known intra-species PPIs with protein-domain profiles to predict PPIs between host and pathogen proteins. Given a set of intra-species PPIs, we identify the functional domains in each of the interacting proteins. For every pair of functional domains, we use Bayesian statistics to assess the probability that two proteins with that pair of domains will interact. We apply our method to the Homo sapiens-Plasmodium falciparum host-pathogen system. Our system predicts 516 PPIs between proteins from these two organisms. We show that pairs of human proteins we predict to interact with the same Plasmodium protein are close to each other in the human PPI network and that Plasmodium pairs predicted to interact with same human protein are co-expressed in DNA microarray datasets measured during various stages of the Plasmodium life cycle. Finally, we identify functionally enriched sub-networks spanned by the predicted interactions and discuss the plausibility of our predictions. AVAILABILITY: Supplementary data are available at http://staff.vbi.vt.edu/dyermd/publications/dyer2007a.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Prediction of protein-protein interactions between human host and a pathogen and its application to three pathogenic bacteria

Molecular understanding of disease processes can be accelerated if all interactions between the host and pathogen are known. The unavailability of experimental methods for large-scale detection of interactions across host and pathogen organisms hinders this process. Here we apply a simple method to predict protein-protein interactions across a host and pathogen organisms. We use homology detection approaches against the protein-protein interaction databases, DIP and iPfam in order to predict interacting proteins in a host-pathogen pair. In the present work, we first applied this approach to the test cases involving the pairs phage T4 -Escherichia coli and phage lambda -E. coli and show that previously known interactions could be recognized using our approach. We further apply this approach to predict interactions between human and three pathogens E. coli, Salmonella enterica typhimurium and Yersinia pestis. We identified several novel interactions involving proteins of host or pathogen that could be thought of as highly relevant to the disease process. Serendipitously, many interactions involve hypothetical proteins of yet unknown function. Hypothetical proteins are predicted from computational analysis of genome sequences with no laboratory analysis on their functions yet available. The predicted interactions involving such proteins could provide hints to their functions.     

Deconstructing host-pathogen interactions in Drosophila

Many of the cellular mechanisms underlying host responses to pathogens have been well conserved during evolution. As a result, Drosophila can be used to deconstruct many of the key events in host-pathogen interactions by using a wealth of well-developed molecular and genetic tools. In this review, we aim to emphasize the great leverage provided by the suite of genomic and classical genetic approaches available in flies for decoding details of host-pathogen interactions; these findings can then be applied to studies in higher organisms. We first briefly summarize the general strategies by which Drosophila resists and responds to pathogens. We then focus on how recently developed genome-wide RNA interference (RNAi) screens conducted in cells and flies, combined with classical genetic methods, have provided molecular insight into host-pathogen interactions, covering examples of bacteria, fungi and viruses. Finally, we discuss novel strategies for how flies can be used as a tool to examine how specific isolated virulence factors act on an intact host.     

Shared molecular strategies of the malaria parasite P. falciparum and the human virus HIV-1

We augmented existing computationally predicted and experimentally determined interactions with evolutionarily conserved interactions between proteins of the malaria parasite, P. falciparum, and the human host. In a validation step, we found that conserved interacting host-parasite protein pairs were specifically expressed in host tissues where both the parasite and host proteins are known to be active. We compared host-parasite interactions with experimentally verified interactions between human host proteins and a very different pathogen, HIV-1. Both pathogens were found to use their protein repertoire in a combinatorial manner, providing a broad connection to host cellular processes. Specifically, the two biologically distinct pathogens predominately target central proteins to take control of a human host cell, effectively reaching into diversified cellular host cellular functions. Interacting signaling pathways and a small set of regulatory and signaling proteins were prime targets of both pathogens, suggesting remarkably similar patterns of host-pathogen interactions despite the vast biological differences of both pathogens. Such an identification of shared molecular strategies by the virus HIV-1 and the eukaryotic intracellular pathogen P. falciparum may allow us to illuminate new avenues of disease intervention.     

Host-pathogen interactions: a proteomic view

Host-pathogen interactions reflect the balance of host defenses and pathogen virulence mechanisms. Advances in proteomic technologies now afford opportunities to compare protein content between complex biologic systems ranging from cells to animals and clinical samples. Thus, it is now possible to characterize host-pathogen interactions from a global proteomic view. Most reports to date focus on cataloging protein content of pathogens and identifying virulence-associated proteins or proteomic alterations in host response. A more in-depth understanding of host-pathogen interactions has the potential to improve our mechanistic understanding of pathogenicity and virulence, thereby defining novel therapeutic and vaccine targets. In addition, proteomic characterization of the host response can provide pathogen-specific host biomarkers for rapid pathogen detection and characterization, as well as for early and specific detection of infectious diseases. A review of host-pathogen interactions focusing on proteomic analyses of both pathogen and host will be presented. Relevant genomic studies and host model systems will be also be discussed.     

Application of proteomics technology for analyzing the interactions between host cells and intracellular infectious agents

Host-pathogen interactions involve protein expression changes within both the host and the pathogen. An understanding of the nature of these interactions provides insight into metabolic processes and critical regulatory events of the host cell as well as into the mechanisms of pathogenesis by infectious microorganisms. Pathogen exposure induces changes in host proteins at many functional levels including cell signaling pathways, protein degradation, cytokines and growth factor production, phagocytosis, apoptosis, and cytoskeletal rearrangement. Since proteins are responsible for the cell biological functions, pathogens have evolved to manipulate the host cell proteome to achieve optimal replication. Intracellular pathogens can also change their proteome to adapt to the host cell and escape from immune surveillance, or can incorporate cellular proteins to invade other cells. Given that the interactions of intracellular infectious agents with host cells are mainly at the protein level, proteomics is the most suitable tool for investigating these interactions. Proteomics is the systematic analysis of proteins, particularly their interactions, modifications, localization and functions, that permits the study of the association between pathogens with their host cells as well as complex interactions such as the host-vector-pathogen interplay. A review on the most relevant proteomic applications used in the study of host-pathogen interactions is presented.     

An Emerging Approach for Parallel Quantification of Intracellular Protozoan Parasites and Host Cell Characterization Using TissueFAXS Cytometry

Characterization of host-pathogen interactions is a fundamental approach in microbiological and immunological oriented disciplines. It is commonly accepted that host cells start to change their phenotype after engulfing pathogens. Techniques such as real time PCR or ELISA were used to characterize the genes encoding proteins that are associated either with pathogen elimination or immune escape mechanisms. Most of such studies were performed in vitro using primary host cells or cell lines. Consequently, the data generated with such approaches reflect the global RNA expression or protein amount recovered from all cells in culture. This is justified when all host cells harbor an equal amount of pathogens under experimental conditions. However, the uptake of pathogens by phagocytic cells is not synchronized. Consequently, there are host cells incorporating different amounts of pathogens that might result in distinct pathogen-induced protein biosynthesis. Therefore, we established a technique able to detect and quantify the number of pathogens in the corresponding host cells using immunofluorescence-based high throughput analysis. Paired with multicolor staining of molecules of interest it is now possible to analyze the infection profile of host cell populations and the corresponding phenotype of the host cells as a result of parasite load.     

Mining Host-Pathogen Protein Interactions to Characterize Burkholderia mallei Infectivity Mechanisms

Burkholderia pathogenicity relies on protein virulence factors to control and promote bacterial internalization, survival, and replication within eukaryotic host cells. We recently used yeast two-hybrid (Y2H) screening to identify a small set of novel Burkholderia proteins that were shown to attenuate disease progression in an aerosol infection animal model using the virulent Burkholderia mallei ATCC 23344 strain. Here, we performed an extended analysis of primarily nine B. mallei virulence factors and their interactions with human proteins to map out how the bacteria can influence and alter host processes and pathways. Specifically, we employed topological analyses to assess the connectivity patterns of targeted host proteins, identify modules of pathogen-interacting host proteins linked to processes promoting infectivity, and evaluate the effect of crosstalk among the identified host protein modules. Overall, our analysis showed that the targeted host proteins generally had a large number of interacting partners and interacted with other host proteins that were also targeted by B. mallei proteins. We also introduced a novel Host-Pathogen Interaction Alignment (HPIA) algorithm and used it to explore similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica. We inferred putative roles of B. mallei proteins based on the roles of their aligned Y. pestis and S. enterica partners and showed that up to 73% of the predicted roles matched existing annotations. A key insight into Burkholderia pathogenicity derived from these analyses of Y2H host-pathogen interactions is the identification of eukaryotic-specific targeted cellular mechanisms, including the ubiquitination degradation system and the use of the focal adhesion pathway as a fulcrum for transmitting mechanical forces and regulatory signals. This provides the mechanisms to modulate and adapt the host-cell environment for the successful establishment of host infections and intracellular spread.     

Protein prenylation: a new mode of host-pathogen interaction

Post translational modifications are required for proteins to be fully functional. The three step process, prenylation, leads to farnesylation or geranylgeranylation, which increase the hydrophobicity of the prenylated protein for efficient anchoring into plasma membranes and/or organellar membranes. Prenylated proteins function in a number of signaling and regulatory pathways that are responsible for basic cell operations. Well characterized prenylated proteins include Ras, Rac and Rho. Recently, pathogenic prokaryotic proteins, such as SifA and AnkB, have been shown to be prenylated by eukaryotic host cell machinery, but their functions remain elusive. The identification of other bacterial proteins undergoing this type of host-directed post-translational modification shows promise in elucidating host-pathogen interactions to develop new therapeutics. This review incorporates new advances in the study of protein prenylation into a broader aspect of biology with a focus on host-pathogen interaction.     

Tissue microbiology provides a coherent picture of infection

The vertebrate host represents an extraordinarily complex and heterogenous environment which pathogenic bacteria of different types able to colonize and infect. This implies that all contributory elements of an infected tissue type or intact host are pre-requisites for full understanding of bacterial pathogenesis. Within this context, intravital techniques allow such studies of infection under realistic conditions within the live host, and with improved detection methods another level of understanding is attained. Here we define this field as tissue microbiology, with its focus placed on monitoring and mimicking host-pathogen interaction within the dynamic micro-ecology significant for infectious niches in the live host. Such all-inclusive approaches promise to more coherently define relevant interactions and angles for disease intervention.     

Lipidomics of host-pathogen interactions

The cell biology of intracellular pathogens (viruses, bacteria, eukaryotic parasites) has provided us with molecular information of host-pathogen interactions. As a result it is becoming increasingly evident that lipids play important roles at various stages of host-pathogen interactions. They act in first line recognition and host cell signaling during pathogen docking, invasion and intracellular trafficking. Lipid metabolism is a housekeeping function in energy homeostasis and biomembrane synthesis during pathogen replication and persistence. Lipids of enormous chemical diversity play roles as immunomodulatory factors. Thus, novel biochemical analytics in combination with cell and molecular biology are a promising recipe for dissecting the roles of lipids in host-pathogen interactions.     

Identification of host factors involved in coronavirus replication by quantitative proteomics analysis

In this study, we applied a quantitative proteomic approach, based on SILAC, to investigate the interactions of coronaviruses with the secretory pathway of the host cell, with the aim to identify host factors involved in coronavirus replication. Comparison of the protein profiles of Golgi-enriched fractions of cells that were either mock infected or infected with mouse hepatitis virus revealed the significant depletion or enrichment of 116 proteins. Although ribosomal/nucleic acid binding proteins were enriched in the Golgi-fractions of mouse hepatitis virus-infected cells, proteins annotated to localize to several organelles of the secretory pathway were overrepresented among the proteins that were depleted from these fractions upon infection. We hypothesized that proteins, of which the abundance or distribution is affected by infection, are likely to be involved in the virus life cycle. Indeed, depletion of a small subset of the affected proteins by using small interfering RNAs identified several host factors involved in coronavirus infection. Transfection of small interfering RNAs targeting either C11orf59 or Golgi apparatus glycoprotein 1 resulted in increased virus replication, whereas depletion of vesicle-trafficking protein vesicle-trafficking protein sec22b enhanced the release of infectious progeny virus. Overexpression of these proteins, on the other hand, had a negative effect on virus replication. Overall, our study shows that the SILAC approach is a suitable tool to study host-pathogen interactions and to identify host proteins involved in virus replication.     

The role of mathematical models of host-pathogen interactions for livestock health and production - a review

Compared with the application of mathematical models to study human diseases, models that describe animal responses to pathogen challenges are relatively rare. The aim of this review is to explain and show the role of mathematical host-pathogen interaction models in providing underpinning knowledge for improving animal health and sustaining livestock production. Existing host-pathogen interaction models can be assigned to one of three categories: (i) models of the infection and immune system dynamics, (ii) models that describe the impact of pathogen challenge on health, survival and production and (iii) models that consider the co-evolution of host and pathogen. State-of-the-art approaches are presented and discussed for models belonging to the first two categories only, as they concentrate on the host-pathogen dynamics within individuals. Models of the third category fall more into the class of epidemiological models, which deserve a review by themselves. An extensive review of published models reveals a rich spectrum of methodologies and approaches adopted in different modelling studies, and a strong discrepancy between models concerning diseases in animals and models aimed at tackling diseases in humans (most of which belong to the first category), with the latter being generally more sophisticated. The importance of accounting for the impact of infection not only on health but also on production poses a considerable challenge to the study of host-pathogen interactions in livestock. This has led to relatively simplistic representations of host-pathogen interaction in existing models for livestock diseases. Although these have proven appropriate for investigating hypotheses concerning the relationships between health and production traits, they do not provide predictions of an animal's response to pathogen challenge of sufficient accuracy that would be required for the design of appropriate disease control strategies. A synthesis between the modelling methodologies adopted in categories 1 and 2 would therefore be desirable. The progress achieved in mathematical modelling to study immunological processes relevant to human diseases, together with the current advances in the generation and analysis of biological data related to animal diseases, offers a great opportunity to develop a new generation of host-pathogen interaction models that take on a fundamental role in the study and control of disease in livestock.